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Отзывы учащихся о курсе Introduction to Data Science in Python от партнера Мичиганский университет

4.5
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Оценки: 23,117
Рецензии: 5,186

О курсе

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....

Лучшие рецензии

PK
9 мая 2020 г.

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

SI
15 мар. 2018 г.

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

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51–75 из 5,108 отзывов о курсе Introduction to Data Science in Python

автор: Bart T C

19 авг. 2018 г.

This course provides very little instruction. I really like learning by trial and error, and I think that is how coding is typically learned. Learning python from stack_exchange, however, is how I was already learning it, and I was doing fine. The whole problem of learning from stack exchange is that you don't know if you are doing things in the best possible way, which can be important for big datasets. There was no discussion of the best practices for complete an assignment, after it was turned in, and, in general, may functions were required to pass the course that were never discussed in the course. The entire weeks lecture could also be watched in about 30 minutes, which seems low to me. Most courses I have taken have at least three hours a week of lecture. I have friends who have taken this same course, and had a similar assessment.

автор: Carl G

9 апр. 2018 г.

Not my style of course. Lectures is a mostly just a list of code snippets without any slides. Instead there is a background of 2 people just staring at their screens the whole time. Does not inspire one to enjoy Data Science as a field. Prefer a narrative explaining why and how with practical tips thrown in. Learning to code is more than just syntax. Good examples are the first chapter in Think Stats by Allen Downey and Andrew Ng's Machine Learning course. In this course the assignments took quite a bit of time to complete since lecture code snippets not very useful. Had to self-learn from web to complete assignments. Also took extra time by some trial and error to get right format of results. A more productive approach was assignments in A

автор: Bas R

10 февр. 2020 г.

Topics covered are interesting as next steps when you have some basic programming skills in Python. However, the introduction and explanation of new concepts feel very rushed; a one minute video on map(), then lambda with a quick exercise without further explanation, followed by list comprehension at the same pace. I often found myself stopping the videos and googling for further explanation to understand what is really going on. If instructors feel that such concepts should be familiar to someone participating in the course, then I'd recommend not covering them at all, rather than rapidly rushing through.

автор: Caroline S

23 окт. 2020 г.

First of all, I DO NOT recommend this course to anyone. Save your money and time and learn the subject in self-studies because that is exactly what you'd be doing if you signed up for the course - only you would be paying for it and experiencing the level of frustration I went through during the completion of this course.

I chose to sign up for this course because I completed the P4E specialization which I loved. But make no mistake, this instructor is no Dr. Chuck.

Key issues:

Instructory videos are extremely brief but cover complex topics, hence you get a very superficial overview of the topic, there is no real "teaching" involved (...which I thought was what I was paying for)

Monotone, non-engaging instructor

Videos do not prepare you for assignments (you will need to spend hours researching the topics on your own)

Poor wording and explanations regarding expectations for the assignment deliverables

автор: Qiang L

17 мар. 2020 г.

I think most of the people mentioned that in the review. There is a HUGE gap between the lecture and the assignment. I am a beginner level of python and know some programming, and I feel really hard to work with the assignment, most of the content in assignment does not cover in lecture. Basically you need to google almost everything you need to finish every assignment. I have been struggling with that since assignment 2. SO what's the point to take a course, why not I just do the assignment directly and google everything. I hope you can change the content and adjust the conection between material and assignment. If you still want do keep the same assignment, try to give more detail in the lecture or have some examples. At least provide some prerequisite course before further into this course.

автор: Eklavya S

5 авг. 2018 г.

This course makes you give up on data science and MOOCs.

Seriously, the content is poorly presented he keeps on speaking , telling 2-3 lines about a function and so on.

I highly recommend stay away from this pathetic specialization.

автор: Nicole B

26 сент. 2020 г.

This course is for knowing what you can do with python in Data science, definitely is not a course to learn python or for people like me who only had basic knowledge of python.

автор: Tural H

5 мар. 2020 г.

Very fast pace, no clarity of the scope and poor leacturing

автор: Wei L

16 июля 2020 г.

Introduction to searching Stackoverflow

автор: PASUPULETI S K

10 мая 2020 г.

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

автор: Andrew

11 февр. 2019 г.

Not nearly enough reference content in lectures. It needs to be made clear students coming from the Python for Everybody course (other Umich course) has a book which I was used to referencing for all of my questions (the class was pretty well self contained and did not require much looking up of concepts). I tried to learn this class the same way I did for the previous one and that totally did not work - I spent wayyyy too much time on my first pandas assignment thinking all of the answers were in lecture/notes. The lecture and notes were very very scant and not well explanative about data structures that are very complicated. Please either write a book or make it more clear how students should learn. Yes, the teacher tells us about stackover flow but I didn't know he was implying for us to use those resources. He should say something like "we don't offer a book with this course so use online resources" and not tip toe around the topic because people paid money to learn so take responsibility and make these changes please. I passed but it was very frustrating at first.

автор: Günter G

4 янв. 2021 г.

This course is really tough, especially the assignments, which are never doable in the estimated 3 hours. That is very frustrating when one is experiencing this.

The course material is mainly a book and a few videos. I needed lots of hours studying on my own to tackle the assignments.

Now I got the certificate and when I look back I can say it was really a tough time but I learned a lot.

автор: Kelam G

17 янв. 2019 г.

It was informative but i felt the assignment part needed more clarification. I faced the problem that even though my solutions were right the autograder gave me lesser marks. I figured out that we must not print to the console. If that was clearly mentioned life would be easier.

автор: Trish P

29 апр. 2019 г.

Solid course. I definitely would not recommend it to someone who doesn't have advanced beginner to intermediate python knowledge, though - while it does a good job at a review level for the necessary python, it really moves through the code details quite quickly.

автор: Ainur A

28 нояб. 2020 г.

The course was undoubtedly challenging (which in itself is a good thing), but at times it was more challenging than it had to be because of some little errors, inconsistecies, wording in the assignment, etc. Sometimes some files wouldn't be where they were supposed to be. Sometimes it took me more time to actually understand what I was supposed to do than actually doing it, because it wasn't really clear what was required. Ok, English is not my first language but I am quite good. Besides, lots of other students complained about confusing wording, so it's not just me. Sometimes some instructions were missing and you'd only realise that after digging through the discussion forums for hours. So, basically there were a few things that made the whole experience more difficult than it had to be, and they weren't about knowledge or skils but about the course "infrastructure".

Before I started this course I'd already taken a few courses on Python programming (on a different platform), including numpy and pandas, but the assinments in this course were still qute difficult for me. You have to do a lot of searching online to get to the solution. In general, I'm all for the idea of having to do more self-learning but the online format is already more challenging than face-to-face learning, so I think it would help to have a few guided exercises between the lectures to understand the material better. Maybe if I had more experience with Python and programming, it would be easier.

One thing that disappointed me was that they recommended a book (optional reading), but you would have to purchase it. Now, if we were studying on campus then we would have access to the university library where we could borrow this book (probably), but as online students we don't have that option. I understand that they can't just upload copywrited books for anyone to access but maybe they could come up with a system that would allow students to somehow access the books temporarily, or make just a few necessary chapters available. I think that's for Coursera to manage, not for the University of Michigan though, because the university can only take advantage of whatever technology coursera can offer (at least that's my understanding).

On a positive note, the discussion forums were quite helpful, and the teaching staff were quick to answer most of the time. I suggest, if you take this course, then take full advantage of the discussion forums.

I also liked that there were two types of assignments (multiple choice quiz and programming). I think that's a great idea.

So, overall, if they made a few improvements to the course, it would be a great one.

автор: Joery D

20 дек. 2020 г.

From my point of view, the content of this course and especially the assignments were very challenging (which i think is positive because you had to look back into the course content and/or Python documentation). This really forces you to actively work with course content.

The main improvement point within this course is the way of how the content is explained and presented; just repeating everything which is written down in the book and trying to explain as much as possible within a short period of time is not the way of teaching from my point of view. Maybe, some slides with some more explanation about a specific topics with (for example) why and how Python is performing as it performs would be more beneficial (instead of immediately jumping in what you have to do to solve a specific problem).

автор: Pragyan

21 сент. 2020 г.

Overall the course is fine. Much of the work is left out to the user, which would be a good thing if the lectures actually spent time discussing a topic. The instructor picks up a topic and shows us one example and is done with it.

I was disappointed with the teaching style. That being said, I did learn a lot in this course. I learnt a lot of stuff, but I wasn't taught much. Some of the topics were really interesting but they are concluded in 5 minutes max.

I really wish the programming walkthrough were more comprehensive and not just "here's how you do this thing, let's move on".

The assignments are challenging, but are poorly worded. Half the time I had to figure out myself what the assignment was asking me to do.

автор: David R Y R

2 нояб. 2020 г.

The course is very task oriented so most of the learning comes from the assignments solution, not from the lectures. Succeeding in the course demands a lot of time for the assignments and quite often you would need to google " pandas how to...". If you want a self-contained course, this is not a good option. However if you want a realistic approach to data science, it may be a good choice.

автор: Marcel K

19 апр. 2019 г.

It would be nice if Coursera could update the Python environment used for the exercises and assignments to something recent. The version they're using (0.19) is fairly old. Every single assignment that I had running against 0.24 had to be altered in some way to work for 0.19.

автор: Lorenzo V ( R P

7 янв. 2021 г.

The assigments' questions were not always clear, but the real issue were the reports from the automatic checks on the answers one submits: puzzling, sto say the least. The rest of the course is OK.

автор: Alejandro A M V

7 нояб. 2020 г.

This course was really challenging, I had to look for information per hours, besides I wanna thank the forum debate. I gave 3 stars because they could improve the teaching techniques.

автор: Michael P R

21 мар. 2019 г.

Good course overall, but more material is required to be learned outside of this class for the required assignments than what is actually taught in the class by a very wide margin

автор: Mr. Q A

26 дек. 2020 г.

The assignments took too long for me to complete .

автор: Jun-Hoe L

9 окт. 2020 г.

Decided to rate this course after I've gone through all 5 courses in the Speclisation. I originally completed this course in January 2020.

So from someone who has completed this Specialisation, I'd say this 5 courses are not worth it.

Here's how I would rank the courses from best to worst:

1. Social Network Analysis: 4.5 stars

2. Applied Machine Learning: 3.5-4 stars

3. Applied Text Mining: 3.5 stars

4. Intro to Data Science: 2

5. Plotting:1

Note that that worst courses are those handled by Professor Brooks himself. His video lectures tend to very superficial (or once in a while, unnecessarily detailed like going into the backend of matplotlib). The assignments on the other hand, are somewhat challenging and go way beyond the video lectures. And that's why you see many comments asking what's the point of purchasing this course when you spend 95% of the time googling? Which is made worse by the outdated autograder which uses and old panda version, and makes googling harder since you had to revert to outdated code.

My advice: Unless you really want the Specialisation cert, I think you should look elsewhere to learn pandas.

автор: Aaron B

19 мар. 2019 г.

Really appreciate this course. Got me started in Python, Pandas, and Jupyter. First week felt like magic. I am giving it a low score because the assignment questions were so ambiguous that it required constant resubmits an scouring the forums. The ratio of learning of course content to required Stack Overflow internet research was way off balance.

I learned a lot but was extremely frustrated and burned a lot of time it what I felt was all the wrong places.

Still grateful for this opportunity. I think the questions can be better explained and tightened up.